A linear convolution-based cancelable fingerprint biometric authentication system

It’s hard to see the modern authentication systems without biometrics technologies. Biometrics systems can be used to access the school and university, for payment confirmation, for access to internet banking. Biometric authentication systems can be based on the face, fingerprint, iris, voice, and other modalities. And all of them have security vulnerabilities. This article is about the attacks on facial biometric systems. It presents the classification of the attacks on facial biometric systems and countermeasures for different types of attacks.

Nowadays, embedded systems run in every setting all around the globe. Recent advances in technology have created many sophisticated applications rich with functionality we have never seen. Nonetheless, security and privacy were a common issue for these systems, whether or not sensitive data can be protected from malicious attacks. These concerns are justified on the grounds that the past of security breaches and the resulting consequences narrate horrific stories concerning embedded systems. The attacks are now evolving, becoming more complex with technological advancements. Therefore, a new way of implementing security in embedded systems must be pursued. This paper attempts to demonstrate the incorporation of security features in fingerprint biometric system in the requirements analysis phase, ensuring the same throughout the system life cycle of embedded systems based on case study. The comparison of various biometric technologies such as face, fingerprint, iris, palm print, hand geometry gait, signature, and keystroke is presented. The aim of this paper includes analyzing, decomposing and transforming the threats and counter-measures identified during the requirements analysis using the abuse case into more specific safety requirements or functions. Furthermore, we have shown that the incorporation of security features into the biometric fingerprint system by analyzing the requirements of the system and providing the main steps for the protection of the biometric system in this paper.

The paper demonstrates a need to study the functional reliability of identification systems and the applicability of methods for ensuring the functional reliability of information systems for biometric identification systems. Methods of ensuring functional reliability of biometric identification systems via providing indicators of reliability of identification devices and introducing redundancy are considered. The applicability of the methods for biometric identification systems is investigated, its advantages and disadvantages are evaluated. Triple modular redundancy scheme has been developed for the program redundancy using N-version programming. An appropriate reliability scheme based on the principles of dual programming has been developed to implement the identification system with one additional redundant element. Based on the NIST statistics of face recognition algorithms for the developed reliability schemes the reliability indicators were evaluated and its advantages over a system consisting of a single non-redundant element have been shown. The proposed reliability schemes are consistent with the practice of ensuring the functional reliability of information systems and can be integrated into biometric identification systems that use biometric data as the main access identifier.

A biometric system is an amalgamation of algorithms that uses an individual’s unique biological data innate physical features (fingerprints, retina, face, ears, etc.) or behavioural features (signature, voice, keystroke dynamics, etc.) or both to authenticate the individual. This essentially makes a biometric system function as a pattern recognition system that helps an individual in any kind of adversarial environment. Hence, like any conventional authentication system, it is vulnerable to the attacks of malicious adversaries that try to manipulate the data and thus decline the security of the system by compromising its robustness. These possible attacks have not been considered while developing most biometric authentication systems. Our work describes multimodal biometrics, its feature extraction methods, research challenges and focuses on the major security issues in both unimodal and multimodal biometric systems. This paper summarizes some robust multimodal systems, their countermeasures and algorithms that have been devised to tackle these security challenges.KeywordsBiometricsMultimodal biometricsBiometric fusionSecurityAttacksCountermeasuresEncryption

To support healthcare professionals in managing the array of information available about patients and the populace, healthcare systems have adopted and continue to incorporate a variety of information and communications technologies (ICT) into the delivery and administration of healthcare. However, isolated ICT systems cannot maximize health outcomes because healthcare delivery occurs within the context of a highly complex system since patients receive healthcare services in a variety of healthcare settings. However, health information exchange (HIE) enables more complete and timely sharing of data and information among ICT systems used in healthcare delivery while supporting provider and patient access to information when and where needed. Thus, this paper presents the development of fingerprint biometric authentication system for health information exchange based the centralized federated HIE model. The proposed HIE system accepts ready-only information from the finger biometric authentication system. The proposed HIE platform has been successfully developed using RESTful API technology and fast health interoperability resource (FHIR) standards to ensure standardized transfer of health information with successful real-time online results. The HIE platform allows for continuous record of patient be made to be available at the point of care subject to authorization by the patient and is applicable in outpatient service delivery, emergency service delivery, public health surveillance, clinical research and clinical trials participants scouting. As a future direction, a clinical decision support system could be implemented on top of the API so as to promote the practice of personalized medicine.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

In recent years, biometric, or "who you are," authentication has grown rapidly in acceptance and use. Biometric authentication offers users the convenience of not having to carry a password, PIN, smartcard, etc. Instead, users will use their inherent biometric traits for authentication and, as a result, risk their biometric information being stolen. The security of users' biometric information is of critical importance within a biometric authentication scheme as compromised data can reveal sensitive information: race, gender, illness, etc. A cancellable biometric scheme, the "BioCapsule" scheme, proposed by researchers from Indiana University Purdue University Indianapolis, aims to mask users' biometric information and preserve users' privacy. The BioCapsule scheme can be easily embedded into existing biometric authentication systems, and it has been shown to preserve user-privacy, be resistant to several types of attacks, and have minimal effects on biometric authentication system accuracy. In this research we present a facial authentication system which employs several cutting-edge techniques. We tested our proposed system on several face databases, both with and without the BioCapsule scheme being embedded into our system. By comparing our results, we quantify the effects the BioCapsule scheme, and its security benefits, have on the accuracy of our facial authentication system.

Biometrics, which is a description of used to describe a computerized recognition of people using their special physical features for safety reasons. Biometric authentication is extremely challenging, necessitating the creation of new, more effective methods in order to distinguish between a genuine, valid feature and a false, self-manufactured, synthetic, or reconstituted sample. The development of biometric detection technology has fuelled by means of recent advancements in computer vision, machine learning, and the process of recognizing patterns. By combining two authentications, the suggested solution aims in order to improve the security of biometric recognition systems. Each modality has a particular set of challenges. When it made sense, adults were employed to assess how well software and technology geared for new-borns performed. The reliability of systems for biometric authentication depends on the type of biometric feature employed, the quality of the data acquired, and the efficiency of the algorithm. The suggested method outperforms prior state-of-the-art methods, and additional biometric data exposes highly critical information that can be utilized to successfully distinguish real features from imitations. Study authors huddled adult iris, retina, fingerprint, and other biometric data in preparation for putting adult biometric identity systems into practice. The pictures were captured using modern day technology, and the iris modality's preliminary analysis procedures were modified to take into consideration the location and slicing of adult lids.

Biometric authentication systems offer a secure and convenient method of verifying the identity of individuals based on their unique physiological or behavioral traits. This paper presents a comprehensive survey of biometric authentication systems, focusing on the principles, technologies, applications, challenges, and future directions in the field. The survey covers various biometric modalities, including fingerprint recognition, iris recognition, facial recognition, keystroke dynamics, voice recognition, and gait analysis. Key topics discussed include the use of multi-modal biometric systems, continuous authentication, machine learning, and AI in biometrics, and biometric encryption. The paper also explores the implementation of biometric authentication in access control systems, financial transactions, healthcare systems, and government and law enforcement. Challenges such as security and privacy concerns, spoofing and impersonation, scalability and integration, and ethical and legal issues are addressed. Future research opportunities include improving accuracy and reliability, enhancing security and privacy, addressing societal and ethical concerns, and integrating biometric authentication with the Internet of Things (IoT).

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Secure access microcontroller system based on fingerprint template with hyperchaotic encryption

Development of biometric human identification algorithms and systems is reviewed. A crisis that prevents them from being used in large computing systems is demonstrated. Comprehensive reliable tests are considered as a way to overcome the crisis. Ways to take into account the influence various factors may have on the reliability of evaluating the practical efficiency of remote human identification algorithms and systems are addressed. A method to determine and calculate quantitative quality criteria of multimodal biometric data is proposed; the possibility of extrapolating the results of testing biometric algorithms to practical application is discussed. Quality functions and the artificiality of biometric data given as the measure of proximity to biometric data registered from an unaware recognition subject are studied.

Score normalization applied to adaptive biometric systems

Several decades ago, attention was directed to biometrics as an alternative to passwords that can be discovered or 'shoulder surfing' by others. Various authentication methods have been provided that rely on the user's biometrics, such as a fingerprint of a face, iris, voice, and others. Unfortunately, ways were found to imitate these visible fingerprints for using them in penetrations. Therefore, many researchers were interested in studying the possibility of using brainwaves for authentication purposes, as relying on hidden vital features increases the difficulty of breaking and imitation. This paper presents an analytical study of the proposed brainwave-based biometric authentication systems. It provides a comparison of signal acquisition methods for the brainwave-based authentication system. Also, the paper classifies brainwaves using its relevant features. It also presents the phases of the brainwave-based authentication system. Finally, it provides a detailed discussion of several factors that affect the accuracy of the brainwave-based authentication system results, and evaluate the compatibility level of the brainwave with the biometric requirements.